The United States faces a unique opportunity to leverage artificial intelligence (AI) to address two pressing issues: the growing number of idle oil and gas wells and the need to shift to cleaner, more sustainable energy systems.
Abandoned and idle wells are a major environmental and public health hazard, releasing harmful greenhouse gases, exposing nearby communities to toxic pollutants, and often leaving costly cleanup burdens on taxpayers.
AI will play a major role in repurposing these idle wells as long-duration energy storage (LDES) assets while simultaneously addressing the environmental and economic burden posed by millions of abandoned and unplugged wells.
Transforming idle oil wells into LDES units is a groundbreaking innovation, with thermal energy storage (TES) offering an efficient solution that combines energy storage with environmental stewardship. Advanced machine learning (ML) analyzes geological data to identify suitable wells, simulates energy storage dynamics, and develops predictive maintenance models for reliable operation.
This approach reduces the environmental and economic impact of abandoned wells while advancing the transition to sustainable energy, redefining the oil and gas industry’s role in this shift. What follows is an exploration of how AI is set to redefine the role of the oil and gas industry in the transition to sustainable energy.
Identifying and prioritizing idle wells
The sheer scale of idle wells in the U.S. is staggering. Estimates suggest that over two million abandoned and unplugged wells exist across the country contributing to methane leaks, groundwater contamination, and other environmental hazards. However, not all wells are suitable for repurposing, and large challenges lie in identifying the best candidates.
AI is a critical component in analyzing complex regulatory and geological datasets to prioritize wells best suited for repurposing. Machine learning algorithms can process massive datasets and detect patterns that allow for the precise selection of candidate wells using parameters, such as depth, formation type, geographic proximity, maintenance records and historical well integrity.
This targeted approach accelerates deployment, reduces environmental risk, and lowers costs associated with repurposing idle wells, making sustainable energy solutions more efficient and scalable.
Optimizing energy storage components
One of AI’s greatest strengths is its ability to optimize complex systems. For repurposed wells used as TES units, this involves tailoring components such as phase change materials (PCMs), high-temperature heat pumps (HTHPs), thermal stratification methods, and heat exchangers to achieve maximum efficiency and performance.
AI-powered simulations can analyze thousands of variable configurations, helping engineers design the best type and combination of PCMs as well as optimizing heat transfer processes within each well. AI-enabled predictive maintenance and real-time monitoring can further enhance the reliability of these TES installations, extending their lifespan and reducing operational costs.
With AI managing and optimizing these systems, each repurposed well can operate at peak efficiency, strengthening the case for repurposing idle wells as a viable solution for storing renewable energy.
Aggregating wells into power networks
In areas where idle wells are clustered, AI can facilitate the aggregation of multiple TES installations into a distributed power network, which could collectively generate substantial energy. This networked approach enables small, distributed storage facilities to act as a single, flexible power resource for regional grids, offering an alternative to centralized power plants.
AI can optimize this network to respond dynamically to grid demand, discharging stored thermal energy during peak times and recharging during periods of low demand. This flexibility is critical as renewable energy sources, like wind and solar, are integrated into the grid, addressing the challenge of the ‘Duck Curve,’ which highlights the steep ramp-up in demand for non-renewable energy as solar output declines in the evening.
By providing stable, on-demand energy, AI powered TES networks can mitigate the impacts of this curve, balance intermittent renewables, and enhance overall grid resilience.
Accelerating the plugging process
AI’s applications extend beyond energy storage and optimization. It can also streamline the process of plugging idle wells, a crucial step in mitigating methane emissions and protecting groundwater. Abandoned wells are often neglected due to the high costs of plugging, but with AI, the process could become faster, safer, and more affordable. Computer vision and sensor data allow AI automated systems to inspect well integrity, detect leaks, and identify structural weaknesses.
This information can guide plugging crews, allowing them to complete the process more efficiently. Streamlining well-plugging operations can help reduce methane emissions, ensuring that the repurposing process aligns with environmental goals.
Creating a sustainable future for petroleum workers
AI has the potential to drive workforce transformation in the oil and gas industry by enabling a seamless transition to roles within the clean energy economy. The petroleum industry is poised for a seismic shift, with traditional roles evolving to meet the demands of the clean energy sector. Through advanced analytics and machine learning, AI can identify skill gaps, design tailored retraining programs, and support workers in adapting to new roles such as operating TES installations, performing predictive maintenance, and optimizing system performance.
By leveraging AI-driven insights, the workforce can evolve alongside technology, ensuring long-term viability and alignment with sustainable energy goals.
In Texas and Louisiana, where the economy is heavily reliant on oil and gas, this transition could be transformative. Meanwhile, in California, where the petroleum industry is gradually being phased out, converting idle wells for LDES could provide alternative employment opportunities and support the state’s decarbonization goals. This shift not only preserves industry expertise but also fosters new job opportunities, bringing economic resilience to communities historically dependent on fossil fuels.
The economic and environmental case
In addition to these operational advantages, the economic and environmental case for applying AI is compelling. Repurposing idle wells avoids the need for new construction, reducing material costs and minimizing environmental disruption.
Meanwhile, converting these wells into energy storage assets rather than remaining environmental liabilities aligns with national and state goals to reduce greenhouse gas emissions.
By offering more efficient means, AI-enabled LDES would support intermittent renewable sources like solar and wind, which, despite their promise, have faced challenges in meeting the demand for round-the-clock power.
With AI’s capability to optimize and streamline deployment, repurposed idle wells would evolve from ‘stranded assets’ into strategic, revenue-generating infrastructure supporting both energy production and environmental sustainability.
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